Optimal Set Of Weights Research Articles

Network anomaly detection using Deep Autoencoder and parallel Artificial Bee Colony algorithm-trained neural network

Cyberattacks are increasingly becoming more complex, which makes intrusion detection extremely difficult. Several intrusion detection approaches have been developed in the literature and utilized to tackle computer security intrusions. Implementing machine learning and deep learning models for network intrusion detection has been a topic of active research in cybersecurity. In this study, artificial neural networks (ANNs), a type of machine learning algorithm, are employed to determine optimal network weight sets during the training phase. Conventional training algorithms, such as back-propagation, may encounter challenges in optimization due to being entrapped within local minima during the iterative optimization process; global search strategies can be slow at locating global minima, and they may suffer from a low detection rate. In the ANN training, the Artificial Bee Colony (ABC) algorithm enables the avoidance of local minimum solutions by conducting a high-performance search in the solution space but it needs some modifications. To address these challenges, this work suggests a Deep Autoencoder (DAE)-based, vectorized, and parallelized ABC algorithm for training feed-forward artificial neural networks, which is tested on the UNSW-NB15 and NF-UNSW-NB15-v2 datasets. Our experimental results demonstrate that the proposed DAE-based parallel ABC-ANN outperforms existing metaheuristics, showing notable improvements in network intrusion detection. The experimental results reveal a notable improvement in network intrusion detection through this proposed approach, exhibiting an increase in detection rate (DR) by 0.76 to 0.81 and a reduction in false alarm rate (FAR) by 0.016 to 0.005 compared to the ANN-BP algorithm on the UNSW-NB15 dataset. Furthermore, there is a reduction in FAR by 0.006 to 0.0003 compared to the ANN-BP algorithm on the NF-UNSW-NB15-v2 dataset. These findings underscore the effectiveness of our proposed approach in enhancing network security against network intrusions.

An Analytical Framework for the Multiplicative Best‐Worst Method

ABSTRACTThe Best‐Worst Method (BWM) is a well‐known Multi‐Criteria Decision‐Making (MCDM) method. Numerous models of BWM have been developed by integrating various distance functions with the core principles of the method. This article addresses the multiplicative model of BWM. We first formulate an optimization model that is equivalent to the existing multiplicative model. This model provides a robust foundation for deriving analytical forms of optimal interval‐weights, the Consistency Index (CI) and the Consistency Ratio (CR). The proposed approach eliminates the need for optimization software, enhancing its ease of implementation and time efficiency. Moreover, the derived analytical form of CR enables it to function as an input‐based consistency measure. We then introduce a secondary objective function to select the best optimal weight set from the collection of all optimal weight sets. Finally, we present numerical examples to illustrate the proposed approach.

An [formula omitted]-cut intervals based Fuzzy Best–Worst Method for Multi-Criteria Decision-Making

To address ambiguity arising from linguistic decision judgments, Guo and Zhao introduced a model of the well-known Multi-Criteria Decision-Making (MCDM) method, Best-Worst Method (BWM), using fuzzy sets, known as Fuzzy Best–Worst Method (FBWM). Despite its versatility, FBWM has limitations, such as criteria-weights being independent of fuzzy comparison values’ shape and reliance on approximate fuzzy arithmetic operations. In response to these limitations, we introduce a novel model of Fuzzy Best–Worst Method based on α-cut intervals. In this model, the calculation of optimal weights involves formulating an optimization problem that utilizes exact fuzzy arithmetic operations defined in terms of α-cut intervals. This approach aims to optimize the entire shape of fuzzy comparison values simultaneously. However, it turns out that, although we have proven the existence of optimal solution for this problem, solving it proves to be challenging. To resolve this issue, we approximate optimal weight set(s) using triangular fuzzy weight sets and assess the Error of Approximation (EA). The approximate weight set with the desired EA is then defuzzified to obtain a crisp weight set. In situations where there are multiple approximate weight sets with the same EA, interval-weights are calculated. Following that, we establish the necessary conditions for a Fuzzy Pairwise Comparison System (FPCS) to be consistent. Using these conditions, we calculate a lower bound of the Consistency Index (CI), providing a measure of the accuracy of weights. Finally, we discuss numerical examples and a real-world scenario, ranking of risk factors in supply chain 4.0, to validate the effectiveness of the proposed model.

Research on optimization method of railway construction scheme based on multidimensional combination weighting and improved grey theory

The optimization of railway construction schemes is a complexity system engineering task with multiple dimensions, diverse conditional constraints, and multifaceted objective assessments. The decision-making and scheme evaluation entail subjectivity, randomness, and fuzziness. To address the comprehensive optimization challenge in construction schemes effectively and efficiently, we investigate an optimization method for railway construction schemes. This method is based on multi-dimensional combination weighting and improved grey theory. After analyzing the primary influencing factors, we established a railway construction plan optimization index system comprising 4 dimensions and 18 factors. The weight combination coefficient is determined using the pros and cons solution distance method, and the optimal weight set for the index is determined through the multi-dimensional combination weighting approach. Utilizing the method of superior and inferior solution distance coupled with grey theory, we ascertain the order of advantages and disadvantages for each construction scheme, subsequently achieving construction scheme optimization. To illustrate this, we employ the optimization process for a high-speed railway section in Guangxi as an exemplar. The verification results indicate that the gray relative closeness values for schemes A, B, and C are 0.7089, 0.4813, and 0.4463, respectively. Scheme A has the highest gray relative closeness value, thus making it the optimal route scheme. The optimal results obtained through this method align with the outcomes of expert validation and existing research, thereby validating the effectiveness and practicality of the model. By employing a multidimensional combination weighting method, the deficiencies of traditional indicator weight calculations are mitigated, resulting in indicator weights that are more reflective of the actual circumstances. At the same time, the application of improvements in the grey theory comprehensive evaluation method enables the integration and computation of indicator data for each construction plan. Through the intuitive representation of grey relative closeness, the advantages and disadvantages of each plan are effectively characterized. This enhances the scientific rigor and applicability of the railway construction plan optimization process. The research findings can serve as a reference for similar railway construction scheme selection problems in the future.

Learning for Cellular Neural Networks Using the GARPLA Algorithm

The main content of this article is to build a hybrid algorithm between genetic algorithm and Recurrent Perceptron Learning Algorithm to determine the weight set for standard cellular neural networks. In particular, the Recurrent Perceptron Learning Algorithm was announced by Gukzelis in 1998 to determine the weight set for CeNNs, however, the nature of the algorithm built according to LMS learning rules means there is still a possibility of local convergence problems of algorithm. Meanwhile, the genetic algorithm originates from natural selection problems and is capable of determining the local optimal domain of the problem. Therefore, the GARPLA hybrid algorithm will fully determine the optimal weight set for the CeNNs network, reducing potential local optimization problems encountered in the RPLA algorithm.

Design of multi-objective guided filter infrared thermal image fusion for hypervelocity impact damages detection

Spacecraft hypervelocity impact damage assessment is becoming increasingly important as the space debris environment deteriorates. Common single-target guided filtering methods are hardly capable of coping with the complex damage infrared thermal image fusion requirements of hypervelocity impacts. In this paper, a two-layer infrared reconstructed image fusion algorithm based on Multi-objective guided filtering (MOGF) is proposed. The aim is to keep the filtered input and output images similar while reducing thermal image noise and retaining as much of the tiny defects as possible in the fusion process. MOGF combines a multi-objective evolutionary optimization algorithm to simultaneously optimize multiple guided filter cost functions in the form of vectors in order to obtain the optimal set of guided filter weight parameters. Given the problem of irregular frontier surfaces in practical multi-objective optimization problems, a weight vector adaptive adjustment strategy is introduced to obtain a more uniform population. Finally, the optimal weight set is passed to the full pixel fusion layer to complete the complex damage infrared reconstructed image fusion. The experiments demonstrate that the proposed fusion method considering multiple targets has higher performance in hypervelocity impact complex damage fusion compared to the general single target fusion method.

A novel framework for FMEA using evidential BWM and SMAA-MARCOS method

This paper presents a novel failure mode and effect analysis (FMEA) framework as a formal design method to ensure safety and reliability. FMEA is used to identify potential failure modes (FMs), and it is crucial to determine the weights of risk factors and prioritize FMs. In this work, we propose a comprehensive framework that integrates the Dempster-Shafer theory, best-worst method (BWM), stochastic multi-objective acceptability analysis (SMAA), and measurement of alternatives and ranking according to compromise solution (MARCOS) to address this problem. To capture the uncertainty caused by the loss of information, the Dempster-Shafer theory is applied for dealing with the uncertainty about risk factors and FMs in terms of linguistic information. Based on the comprehensive evidential preference interval vectors of risk factors constructed by Dempster-Shafer theory, an evidential BWM combined with SMAA is proposed to determine the optimal set of risk factor weights. Meanwhile, based on the constructed interval belief interval decision matrix of FMs to risk factors constructed by Dempster-Shafer theory, an evidential SMAA-MARCOS method is proposed for determining the risk priority of FMs. Further, we conduct a case study to evaluate the risk of equipment in an automobile manufacturing enterprise. A sensitivity and comparative analysis are also conducted to demonstrate the effectiveness and superiority of the proposed framework.

Deep clustering of Lagrangian trajectory for multi-task learning to energy saving in intelligent buildings using cooperative multi-agent

The intelligent buildings provided various incentives to get highly inefficient energy-saving caused by the non-stationary building environments. In the presence of such dynamic excitation with higher levels of nonlinearity and coupling effect of temperature and humidity, the HVAC system transitions from underdamped to overdamped indoor conditions. This led to the promotion of highly inefficient energy use and fluctuating indoor thermal comfort. To address these concerns, this study develops a novel framework based on deep clustering of lagrangian trajectories for multi-task learning (DCLTML) and adding a pre-cooling coil in the air handling unit (AHU) to alleviate a coupling issue. The proposed DCLTML exhibits great overall control and is suitable for multi-objective optimisation based on cooperative multi-agent systems (CMAS). The framework of DCLTML is used greedy iterative training to get an optimal set of weights and tabulated as a layer for each clustering structure. Such layers can deal with the challenges of large space and its massive data. Then the layer weights of each cluster are tuned by the Quasi-Newton (QN) algorithm to make the action sequence of CMAS optimal. Such a policy of CMAS effectively manipulates the inputs of the AHU, where the agents of the AHU activate the natural ventilation and set chillers into an idle state when the outdoor temperature crosses the recommended value. So, it is reasonable to assess the impact potential of thermal mass and hybrid ventilation strategy in reducing cooling energy; accordingly, the assigning results of the proposed DCLTML show that its main cooling coil saves >40% compared to the conventional benchmarks. Besides significant energy savings and improving environmental comfort, the DCLTML exhibits superior high-speed response and robustness performance and eliminates fatigue and wear due to shuttering valves. The results show that the DCLTML algorithm is a promising new approach for controlling HVAC systems. It is more robust to environmental variations than traditional controllers, and it can learn to control the HVAC system in a way that minimises energy consumption. The DCLTML algorithm is still under development, but it can potentially revolutionise how HVAC systems are controlled.

An Incremental Approach for Understanding Collision Avoidance of an Industrial Path Planner

Autonomous Driving Systems (ADSs) are complex systems that must consider different aspects such as safety, compliance to traffic regulations, comfort, etc. The relative importance of these aspects is usually balanced in a weighted cost function. However, there is generally no optimal set of weights, and different driving situations may require different weights values to guarantee a safe drive. Recent testing approaches can generate diverse driving scenarios in which different ADS configurations lead to various degrees of hazard. These tests need to be properly analyzed to improve the ADS's safety. In this paper, we propose \paolo{an analysis approach} that is able to assess the relation between the ADS configurations and the level of hazard that is obtained in some particular traffic situations. The approach uses fuzzification to partition ADS weights in different categories, and a spectrum-based analysis to identify which weights categories are related to hazard and safety. The occurrence of a hazard could be due to a single weight or to combinations of two or more weights. For scalability, the approach performs an incremental analysis, in which first single weights are considered, and then weight combinations of higher order. The approach has been applied to an industrial path planner.

A New Method for Determination of Optimal Borehole Drilling Location Considering Drilling Cost Minimization and Sustainable Groundwater Management.

Drilling boreholes for the exploration of groundwater incurs high cost with potential risk of failures. However, borehole drilling should only be done in regions with a high probability of faster and easier access to water-bearing strata, so that groundwater resources can be effectively managed. However, regional strati-graphic uncertainties drive the decision of the optimal drilling location search. Unfortunately, due to the unavailability of a robust solution, most contemporary solutions rely on physical testing methods that are resource intensive. In this regard, a pilot study is conducted to determine the optimal borehole drilling location using a predictive optimization technique that takes strati-graphic uncertainties into account. The study is conducted in a localized region of the Republic of Korea using a real borehole data set. In this study we proposed an enhanced Firefly optimization algorithm based on an inertia weight approach to find an optimal location. The results of the classification and prediction model serve as an input to the optimization model to implement a well-crafted objective function. For predictive modeling a deep learning based chained multioutput prediction model is developed to predict groundwater-level and drilling depth. For classification of soil color and land-layer a weighted voting ensemble classification model based on Support Vector Machines, Gaussian Naïve Bayes, Random Forest, and Gradient Boosted Machine is developed. For weighted voting, an optimal set of weights is determined using a novel hybrid optimization algorithm. Experimental results validate the effectiveness of the proposed strategy. The proposed classification model achieved an accuracy of 93.45% and 95.34% for soil-color and land-layer, respectively. While the mean absolute error achieved by proposed prediction model for groundwater level and drilling depth is 2.89% and 3.11%, respectively. It is found that the proposed predictive optimization framework can adaptively determine the optimal borehole drilling locations for high strati-graphic uncertainty regions. The findings of the proposed study provide an opportunity to the drilling industry and groundwater boards to achieve sustainable resource management and optimal drilling performance.

Smart Seismic Modeling - Artificial Intelligence in the Petroleum Industry

Vertical seismic profile (VSP) data have many known advantages, if being compared to seismic data such as broader bandwidth, less attenuation, clearer under well true depth (TD) image, multiple noise differentiation, shear wave information, direct attenuation and anisotropy measurements, higher fidelity amplitude versus offset (AVO) effect, etc. In this paper, we share the bottom-up approach that aims to maximize the information extracting from borehole seismic, in conjunction with seismic and well log data, to generate a geological model by integrating full wave-field modeling (FWM) to deep learning techniques, called smart seismic modeling (SSM) project. The study starts using data at well locations, with vertical seismic profile (VSP) forward modeling process. The 2D rock physics model is thus built from the prior well logs and the major interpreted seismic horizons and faults. By altering rock-physics properties values and thickness of each layer, the model has been multiplied thousand times. The rock-physics models and their simulated seismic traces are fed, as labels and inputs, respectively, into a 2D deep learning network. The network then extracts the non-linear relationships between inputs and labels so that thickness and rock-physics properties can simultaneously be regressed with least squared error loss function. Optimal trained weight set (artificial intelligence engine) is finally used to predict wellbore’s surrounding geologic structures and rock properties for all the wells in the study area. To make sure machine learning can provide a good work, similar training and prediction process is carried out from surface seismic forward modeling. Not only is it used for rock property prediction at the area apart from wells, but also for cross checking the prediction at well location. All 2D predicted rock-property models are merged by multi-point statistics (MPS) method using hard data from wells’ prediction and trends from actual VSP processing, such as anisotropy, 2D Qp, Qs, for a complete 3D geologic model. By doing with two different modeling processes, i.e., VSP forward modeling and surface seismic forward to cross check the results, results can obtain from this study are: 1)high quality static model for field development; 2)high quality dynamic model for field monitoring; 3)robust artificial intelligence engines for variety types of geology. In this study, we introduce the SMM approach and share promising successes in applying deep learning in rock property inversion process. The successful showcase has proved that deep learning technique can be used to extract any kind of rock properties from the big data of VSP, well log and of seismic.

A Multi-Modal Deep Learning-Based Decision Support System for Individualized Radiotherapy of Non-Small Cell Lung Cancer

<h3>Purpose/Objective(s)</h3> In a complex disease such as cancer, interactions between the tumor and host can exist at the molecular, cellular, tissue, and organism levels. The disease and its evolution may be present in multiple modalities across scales such as clinical, genomic, molecular, pathological, and radiological imaging, which poses a challenge to clinicians in making treatment decisions. This is especially true in cancer radiotherapy, where troves of routinely generated data include clinical data (e.g., diagnosis and prognosis), radiotherapy data (e.g., treatment plans and delivered doses), and the anatomical and functional image data (e.g., CT, MRI, PET). AI can be a powerful tool to explore the many potential care paths in the radiotherapeutic management of cancers and to generate the best treatment option specific to each individual patient while maximizing benefits and minimizing toxicities. In this work, we aim to develop a multi-modal AI-empowered clinical decision support system for personalized radiotherapy of non-small cell lung cancer patients. <h3>Materials/Methods</h3> With IRB approval, data of 217 non-small cell lung cancer patients was obtained, which included treatment planning data, images, and clinical notes. Factors extracted from treatment planning system were focused on clinical, demographic, and dose-volume indexes. Radiomics features were extracted using open-source software. The clinical notes were analyzed using natural language processing to cluster phrases into predictive features. The toxicities of each patient were ranked on a RTOG scale of 0-5 on radiation pneumonitis severity, while the outcomes were ranked on a scale of 0-3 based on tumor reduction over 3 years per RECIST 1.1 guidelines. The consolidated and normalized data was input into a deep neural network with five hidden layers. Using 5-fold cross validation testing (172 training patients and 43 testing patients x 5), an optimal set of weights was determined. <h3>Results</h3> The accuracy of the model was 89.4% for predicting whether radiation pneumonitis was severe (RTOG >=2) and was 71.0% in predicting whether the treatment was successful after 3 years. This approach was superior to traditional approaches focusing on a single datatype. The model performed well with up to 5% of the training data missing, reflecting real clinical challenges. <h3>Conclusion</h3> In this work, we've developed a multi-modality-based decision support system for radiation therapy and demonstrated its efficacy in predicting radiation-induced pneumonitis and patient outcome. Future work will be on automating the curation of structured and unstructured patient data to achieve adaptive personalized radiotherapy for more robust and comprehensive clinical decision support in the clinic.

Ecpoc: an evolutionary computation-based proof of criteria consensus protocol

Recently, blockchain technology has been applied in many domains in our life. Blockchain networks typically utilize a consensus protocol to achieve consistency among network nodes in a decentralized environment. Delegated Proof of Stake (DPoS) is a popular mechanism adopted in many networks such as BitShares, EOS, and Cardano because of its speed and scalability advantages. However, votes that come from nodes on a DPoS network tend to support a set of specific nodes that have a greater chance of becoming block producers after voting rounds. Therefore, only a small group of nodes can be selected to become block producers. To address this issue, we propose a new protocol called Evolutionary Computation-based Proof of Criteria (ECPoC), which uses ten criteria to evaluate and select a new block procedure in each round. Next, a set of optimal weights used for maximizing the network’s decentralization level is identified through the use of evolutionary computation algorithms. The experimental results show that our consensus significantly enhances the degree of decentralization in the selection process of witness nodes compared to DPoS. As a result, ECPoC facilitates fairness between nodes and creates momentum for blockchain network development

Optimized short-forms of the Cognitive Distortions Questionnaire

IntroductionThe Cognitive Distortions Questionnaire (CD-Quest) is a self-report questionnaire that assesses common cognitive distortions. Although the CD-Quest has excellent psychometric properties, its length may limit its use. MethodsWe attempted to develop short-forms of the CD-Quest using RiskSLIM – a machine learning method to build short-form scales that can be scored by hand. Each short-form was fit to maximize concordance with the total CD-Quest score for a specified number of items based on an objective function, in this case R2, by selecting an optimal subset of items and an optimal set of small integer weights. The models were trained in a sample of US undergraduate students (N = 906). We then validated each short-form on five independent samples: two samples of undergraduate students in Brazil (Ns = 182, 183); patients with depression in Brazil (N = 62); patients with social anxiety disorder in the US (N = 198); and psychiatric outpatients in Turkey (N = 269). ResultsA 9-item short-form with integer scoring was created that reproduced the total 15-item CD-Quest score in all validation samples with excellent accuracy (R2 = 90.4-93.6%). A 5-item ultra-short-form had good accuracy (R2 = 78.2-85.5%). DiscussionA 9-item short-form and a 5-item ultra-short-form of the CD-Quest both reproduced full CD-Quest scores with excellent to good accuracy. These shorter versions of the full CD-Quest could facilitate measurement of cognitive distortions for users with limited time and resources.

Short-Term PV Power Forecasting Using a Regression-Based Ensemble Method

One of the most critical aspects of integrating renewable energy sources into the smart grid is photovoltaic (PV) power generation forecasting. This ensemble forecasting technique combines several forecasting models to increase the forecasting accuracy of the individual models. This study proposes a regression-based ensemble method for day-ahead PV power forecasting. The general framework consists of three steps: model training, creating the optimal set of weights, and testing the model. In step 1, a Random forest (RF) with different parameters is used for a single forecasting method. Five RF models (RF1, RF2, RF3, RF4, and RF5) and a support vector machine (SVM) for classification are established. The hyperparameters for the regression-based method involve learners (linear regression (LR) or support vector regression (SVR)), regularization (least absolute shrinkage and selection operator (LASSO) or Ridge), and a penalty coefficient for regularization (λ). Bayesian optimization is performed to find the optimal value of these three hyperparameters based on the minimum function. The optimal set of weights is obtained in step 2 and each set of weights contains five weight coefficients and a bias. In the final step, the weather forecasting data for the target day is used as input for the five RF models and the average daily weather forecasting data is also used as input for the SVM classification model. The SVM output selects the weather conditions, and the corresponding set of weight coefficients from step 2 is combined with the output from each RF model to obtain the final forecasting results. The stacking recurrent neural network (RNN) is used as a benchmark ensemble method for comparison. Historical PV power data for a PV site in Zhangbin Industrial Area, Taiwan, with a 2000 kWp capacity is used to test the methodology. The results for the single best RF model, the stacking RNN, and the proposed method are compared in terms of the mean relative error (MRE), the mean absolute error (MAE), and the coefficient of determination (R2) to verify the proposed method. The results for the MRE show that the proposed method outperforms the best RF method by 20% and the benchmark method by 2%.

Monthly North Atlantic Sea Level Pressure Reconstruction Back to 1750 CE Using Artificial Intelligence Optimization

AbstractMain modes of atmospheric variability exert a significant influence on weather and climate at local and regional scales on all time scales. However, their past changes and variability over the instrumental record are not well constrained due to limited availability of observations, particularly over the oceans. Here we couple a reconstruction method with an evolutionary algorithm to yield a new 1° × 1° optimized reconstruction of monthly North Atlantic sea level pressure since 1750 from a network of meteorological land and ocean observations. Our biologically inspired optimization technique finds an optimal set of weights for the observing network that maximizes the reconstruction skill of sea level pressure fields over the North Atlantic Ocean, bringing significant improvements over poorly sampled oceanic regions, as compared to non-optimized reconstructions. It also reproduces realistic variations of regional climate patterns such as the winter North Atlantic Oscillation and the associated variability of the subtropical North Atlantic high and the subpolar low pressure system, including the unprecedented strengthening of the Azores high in the second half of the twentieth century. We find that differences in the winter North Atlantic Oscillation indices are partially explained by disparities in estimates of its Azores high center. Moreover, our reconstruction also shows that displacements of the summer Azores high center toward the northeast coincided with extremely warm events in western Europe including the anomalous summer of 1783. Overall, our results highlight the importance of improving the characterization of the Azores high for understanding the climate of the Euro-Atlantic sector and the added value of artificial intelligence in this avenue.

Two-Stage Cooperative/Non-Cooperative Game DEA Model with Decision Preference: A Case of Chinese Industrial System

Decision preference, the most common constraint in the decision-making process, is essential in efficiency evaluation and decision making. Therefore, quantitatively setting the weight of decision-making preference is critical to the field of data envelopment analysis (DEA) efficiency research. This study introduces a decision preference between desirable and undesirable outputs, the order and ratio of the decision preference between sub-stages, and constructs a two-stage cooperative/noncooperative game DEA model. The model is used to evaluate and compare the efficiency of the Chinese regional industrial system over the period 2011–2015 from the perspective of a two-stage cooperative/noncooperative game. Research on introducing desirable/undesirable output preference relation constraints reveals the following: (1) In the noncooperative game evaluation framework that considers undesirable outputs, (i) determining a set of common optimal output preference weights can help decision makers verify the initial subjective decision preference or quantitatively set better decision preference weights to obtain more reliable evaluation results, and that (ii) the method of setting the proportion preference weights between stages based on the perspective of output proportion can effectively avoid the efficiency deviation of the input proportion method in the disposal of undesirable outputs. (2) The efficiency of the Chinese industrial system (i) under the non-cooperative game relationship during the study period is mainly affected by low pollution control efficiency components, in conformity to the “Cannikin Law,” and (ii) there are marked differences in the overall efficiency and substage efficiency of China's industries under the cooperative game relationship; and the imbalance in industrial development among regions is significant.

Distributed computation of fast consensus weights using ADMM

In time-critical multi-agent tasks, it is important for the agents to reach consensus as fast as possible. In this paper, we consider the problem of computing the weights in the weighted-average consensus protocol that achieve average consensus with an optimal per-step convergence factor. Most of the work in the literature either computes these optimal set of weights in a centralized manner, which requires global information about the network that may not be available, or computes a suboptimal set of weights, which are slow in achieving consensus. We propose an iterative, distributed algorithm to compute a set of weights that achieve an optimal convergence factor. We give theoretical guarantees of the convergence of the algorithm. Through numerical examples, we show that our method performs better than other distributed methods of computing weights for consensus, and it matches the performance of the centralized optimal method.

Spectral-element method with an optimal mass matrix for seismic wave modelling

The spectral-element method (SEM), which combines the flexibility of the finite element method (FEM) with the accuracy of spectral method, has been successfully applied to simulate seismic wavefields in geological models on different scales. One kind of SEMs that adopts orthogonal Legendre polynomials is widely used in seismology community. In the SEM with orthogonal Legendre polynomials, the Gauss-Lobatto-Legendre (GLL) quadrature rule is employed to calculate the integrals involved in the SEM leading to a diagonal mass matrix. However, the GLL quadrature rule can exactly approximate only integrals with a polynomial degree below 2N-1 (N is the interpolation order in space) and cannot exactly calculate those of polynomials with degree 2N involved in the mass matrix. Therefore, the error of the mass matrix originating from inexact numerical integration may reduce the accuracy of the SEM. To improve the SEM accuracy, we construct a least-squares objective function in terms of numerical and exact integrals to increase the accuracy of the GLL quadrature rule. Then, we utilise the conjugate gradient method to solve the objective function and obtain a set of optimal quadrature weights. The optimal mass matrix can be obtained simultaneously by utilising the GLL quadrature rule with optimal integration weights. The improvement in the numerical accuracy of the SEM with an optimal mass matrix (OSEM) is demonstrated by theoretical analysis and numerical examples.

An optimized weighted voting based ensemble model for DDoS attack detection and mitigation in SDN environment

In recent years, software defined networking (SDN) has risen to prominence as a cutting-edge and promising networking approach. SDN is more secure and immune to DDoS attacks than traditional networks due to the bifurcation of the control and data planes, a global perspective of the whole network, centralized network control, and many other features. SDN still seems to be vulnerable to new DDoS attacks that can target the Application plane, control plane, or data plane, as well as the SDN architectural communication channels. In this paper, we propose a novel optimized weighted voting ensemble model to detect DDoS attack in an SDN environment. The proposed ensemble employs six base classifiers (two SVMs, two Random forests, and two Gradient Boosted Machines) that are differentiated by hyperparameter values. The optimal set of weights are identified by a novel hybrid metaheuristic optimization algorithm (BHO). Our approach eliminates false negatives by employing a novel dynamic fitness function. For training the ensemble framework we used CIC-DDoS2019 dataset. To examine the proposed model’s performance, a testbed is developed using mininet and POX controller and loaded with multiple datasets. Our model outperforms all the recent approaches, achieving a low variance and high classification accuracies of 99.4163% and 99.3591% on CIC-DDoS2019 and CAIDA-2007 datasets respectively.